Investigation on Antioxidant Activity and Different Metabolites of Mulberry (Morus spp.) Leaves Depending on the Harvest Months by UPLC–Q-TOF-MS with Multivariate Tools

The changes in active components in mulberry leaves harvested in different months and their antioxidant activities were investigated. Ultra-high-performance liquid chromatography–tandem quadrupole time-of-flight mass spectrometry (UPLC–Q-TOF-MS) with multivariate statistical tools was used to investigate the chemical constituents in the extracts of mulberry leaves. The results indicated that mulberry leaves were rich in phenolic acids, flavonoids, organic acids, and fatty acid derivatives. In addition, 25 different compounds were identified in the different batches of mulberry leaves. The 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity was measured to evaluate the in vitro antioxidant activities of mulberry leaves. Among the four batches, batch A, harvested in December, exhibited the strongest DPPH radical-scavenging activity, while batch B, harvested in March, showed the weakest activity. This was related to the total phenolic content in the mulberry leaves of each batch. The optimal harvest time of mulberry leaves greatly influences the bioactivity and bioavailability of the plant.


Introduction
Mulberry (Morus spp.) has been widely cultivated in many Asian countries, such as China, India, Korea, Japan, and Thailand. Mulberry leaves, commonly used as food for silkworms in sericulture, have been commercially available as a kind of special tea or drink in many Asian countries [1][2][3]. However, mulberry leaves are also traditionally applied as folk medicine to treat fever, protect the liver, improve eyesight, strengthen the joints, facilitate the discharge of urine, and lower blood pressure [4][5][6]. Modern pharmacological research has revealed that mulberry leaves also have a broad range of biological activities, such as antioxidant [7,8], anti-inflammatory [9][10][11], anti-bacterial [12], anti-hypertensive [13], anti-atherogenic [14], and anti-cancer [15] effects. Studies of the chemical constituents of mulberry leaves have shown that mulberry leaves are rich in phenols, flavonoids, alkaloids, amino acids, polysaccharides, and steroids [16][17][18][19][20]. The species and content of secondary metabolites may vary with the different growth stages, which results in potentially different pharmacological activities. The literature has reported that the degree of maturity and the harvest time of mulberry leaves significantly affected the content of nutritional and functional components [21][22][23][24].
The phenols have been reported to be one of the main components in mulberry leaves. The phenolic content in mulberry leaves has been found to be greatly influenced by the leaf age (tips, young, and old leaves) [23] and seasonal changes [24]. The tips of the leaves were taken from positions 1 to 3 from the top of each branch; young leaves were taken from positions 4 to 6; and old leaves were taken from positions 7 to 10. The results showed that the phenolic content in the tips of leaves was higher [23]. The in vitro antioxidant capacity of different-aged mulberry leaves was detected and compared with clear superoxide radical (O 2 −• ), DPPH free radical scavenging, hydroxyl radical scavenging, and Fe 2+ chelating and reducing activities, respectively [25,26]. Mulberry leaves collected in May were considered to be preferred because of their higher phenolic content [21,22]. There were differences in the levels of the seven phenolic compounds (chlorogenic acid, benzoic acid, rutin, isoquercitrin, astragalin, quercetin-3-O-(6-O-malonyl)-β-D-glucoside, and kaempferol-3-O-(6-O-malonyl)-β-D-glucoside) during the growing seasons, with leaves collected from April to October. However, the literature only reported the content changes in a very few polyphenolic compounds in mulberry leaves, and the studies were relatively limited. The quantitative changes in other functional constituents in mulberry leaves need to be clarified.
Thus, the quantitative changes in functional constituents in mulberry leaves according to the harvest month were studied. In the Huzhou area (a traditional sericulture area), March, April, May, and December represent the growth periods and different picking periods for mulberry leaves. Mulberry leaves have different uses in different periods; for example, the young shoots of mulberry leaves can be used for cooking in March, big mulberry leaves are plucked in April and May to feed silkworms, and old mulberry leaves are picked after the frost for herbal tea in December. Therefore, the mulberry leaves collected in March, April, May, and December were selected and studied using an ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) technique. The UPLC-Q-TOF-MS technology has the required sensitivity for fast, high-resolution separations and also facilitates the structure elucidation and identification of fragmentation patterns [27]. The antioxidant activities of mulberry leaves at different harvest times were also evaluated using the DPPH assay. Meanwhile, the total phenolic content of mulberry leaves in each batch was measured. An investigation of the influence of mulberry leaves harvested in different months on these variables would be helpful to understand the health benefits of mulberry leaves.

Optimization of Extraction Conditions
Taking the main seven compounds in mulberry leaves as target compounds (as shown in Figure S1), the solvent extraction conditions of mulberry leaves were optimized by single-factor experiments. The extraction conditions, including extraction solvents (Water, N-hexane, ethyl acetate, methanol, ethanol, 15% ethanol solution, 30% ethanol solution, 45% ethanol solution, 60% ethanol solution, 75% ethanol solution, and 90% ethanol solution), extraction methods (maceration, heat-reflux extraction, and ultrasonic extraction), liquid/solid ratios (10-40 mL/g), extraction times (20-50 min), and extraction temperatures (25-45 • C), were all tested and the results are shown in Figures S2-S7, respectively. According to the number, separation, and areas of the peaks, the preferred extraction conditions include an extraction solvent of 75% ethanol solution, an extraction method of ultrasonic extraction, a liquid/solid ratio of 30 mL/g, an extraction time of 30 min, and an extraction temperature of 35 • C.
To acquire the maximum sensitivity for most constituents, the Q-TOF-MS and Q-TOF-MS/MS parameters were also optimized in both positive and negative modes. The drying gas was run at various flow rates (6.0, 8.0, and 10.0 L/min) and temperatures (180, 200, and 220 • C). The optimum conditions were determined as follows: The capillary voltage was 3800 V in the negative mode; the dry gas flow rate was 6.0 L/min; the nebulizer gas pressure was 0.8 bar; and the dry gas temperature was set at 200 • C.

Identification of the Compounds by UPLC-Q-TOF-MS
UPLC-Q-TOF-MS coupled with an electrospray ionization (ESI) ion source in the negative mode was performed to analyze the compounds of mulberry leaf extracts using 75% ethanol extracts of batch B. The base peak chromatogram (BPC) of mulberry leaf extracts is shown in Figure 1. The information from UPLC-Q-TOF-MS data and the names of speculated compounds are summarized in Table 1.    As is shown in Table 1 and Figure 1, a total of 77 compounds were detected based on retention time (RT), exact mass data, fragment information, and molecular formulas reported in the literature, and 65 chemical structures were tentatively deduced and identified. Among them, there were 5 organic acids, 1 vitamin derivative, 15 phenolic compounds, 31 flavonoids, 6 fatty acid derivatives, 3 terpenoids, 2 quinonoids, 1 lignan, and 1 terpene. Flavonoid glycosides were the main compounds found in the 75% ethanol extracts of mulberry leaves.
Compounds 1-6 were identified as organic acids and vitamin derivatives by comparison of their RT, accurate molecular ions, and characteristic fragment ions with those reported in the literature or by the MS-DIAL database . Compounds 61, 63, 65, 70, 75, and 77 were identified as fatty acid derivatives by comparison with RT, accurate molecular ions, and characteristic fragment ions, as mentioned in the literature [28].
Compounds 7-18, 22, 23, and 64 were identified as phenolic compounds. In the MS spectra, all of these compounds showed similar fragmentation pathways by losing a glucose substituent (162 Da) from the precursor ions, and the continuous losses of H 2 O and CO 2 from the fragment ions.
Similarly, compounds 21, 24, 31, 32, 37-40, 43-50, and 54-56 were also tentatively identified according to the accurate molecular formulas, the fragmentation pathways, the reference substances, and the reported literature [27][28][29][30]33,35]. Most of these compounds contain a common 15-carbon polyphenolic skeleton and glycosides and are easily deglycosylated to lose the glucose units during MS fragmentation [45,46]. PCA analysis is a commonly used unsupervised discriminant analysis method that can reduce the dimensional display of multi-dimensional data. The projection points are obtained to determine the position of this group of data by projecting the scores of each variable in a group of data onto the principal components. PCA analysis can display multidimensional information in a two-dimensional way. Samples gather or separate based on their differences: similar samples will gather, and different samples will separate from each other. QC samples can be used for standardization. They can be used to simulate the difference in signals in the data acquisition process and correct the error of the instrument. The QC data can be used as a training set to establish a prediction model.
The PCA score chart of four batches of mulberry leaves harvested in different months is shown in Figure 2. The model was excellent, with the goodness-of-fit parameter R 2 X (97.6%) and the predicted fitting parameter Q 2 (96.5%) according to the four principal components. Quality control (QC) samples were gathered near the origin, proving that the experimental operation error and instrument detection error had little influence during the experiment. The intra-group data of each batch of mulberry leaves was well aggregated. The mulberry leaves harvested in different months were well separated between groups, and there were no obvious abnormal points. Among them, mulberry leaves collected in December 2019 (batch A) were alone at the extreme edge of the third quadrant, which was separated from the other three batches. The other three batches of samples were mainly in the first and fourth quadrants, and these samples were collected in the spring and summer of 2020. The PCA result plot was the same as the actual predicted result. It was feasible to investigate the compositional differences in mulberry leaves harvested in different months in an unsupervised manner.

Comparisons of Different Batches of Mulberry Leaves by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)
To further explore the differential compounds among the mulberry leaves harvested in different months, a pairwise comparison of OPLS-DA was carried out. As is shown in Figure 3, a volcano plot combined with the VIP value was used to analyze the differential substances. The results are shown in Table 2.
The purple mark is the differential compound with VIP = 2-3, and the difference was significant. The blue points are differential compounds with VIP > 3, which showed very high differences. The size of the VIP value was reflected by the size of each mark. It showed that the mulberry leaves of batch A showed many different metabolites from batches B, C, and D, which also corresponded to the harvest time. It also proved that the substances in the mulberry leaves harvested in December were very different from those in the other mulberry leaves. Similarly, since the harvest time interval was only one month, there were nearly no significant differences in batches B, C, and D. The results are shown in Table 2.  Note: "+" indicated that the content of the differential compound in the previous batch was more than that in the latter. "-" was the opposite. The more plus signs showed the greater the difference in the content.
By comparison, it was found that there were significant differences in the types and content of compounds between batch A and the other three batches. There was little difference between batches B, C, and D. The contents of compounds 40, 32, 69, 66, 72, 18 A and B. Compounds 40, 32, 69, and 72 were found in relatively high amounts in batch A; the same was true for compound 66 in batch B. There were more different compounds between A and C/ D, including compounds  69, 66, 72, 18, 37, 30, 60, 59, 38, 64, 58, 65, 39, 8, 51, 53, 40, and 10. Compounds 69, 72,  37, 60, 59, 38, and 40 had a higher content in batch A, while compound 65 had a higher content in batch C/D. Since the harvest time interval was only one month, there were nearly no significant differences in batches B, C, and D. Table 2 also shows that the differential compounds were mainly fatty acid derivatives and flavonoids.

DPPH Assay and Assays for Total Phenolics
The DPPH is a stable free radical, which is reduced to α,α-diphenyl-β-picrylhydrazine by reacting with an antioxidant. Antioxidants interrupt free radical chain oxidation by donating hydrogen from hydroxyl groups to form a stable end-product that does not initiate or propagate further oxidation of lipids [16,47]. The results of the free radical-scavenging activity of different mulberry leaves are shown in Figure 4. All the extracts demonstrated significant inhibitory activity against the DPPH radicals. The IC 50 of batches A, B, C, and D were 0.057, 0.176, 0.162, and 0.090 mg/mL, respectively. The results of the DPPH scavenging activity of mulberry leaves in different harvest months showed that the free-radical-scavenging activity of mulberry leaves in batch A was the strongest. The free radical scavenging activity in batches C and D was similar, while batch B was the weakest. This showed a certain regularity in the growth period. Batch A of mulberry leaves after frost exhibited the strongest free-radical-scavenging activity. Among the three batches B, C, and D of mulberry leaves, the free-radical-scavenging activity increased with the growth period. Combined with the results of the differential component analysis, this showed that the mulberry leaves of batch A were quite different from batches B, C, and D. The difference was mainly in flavonoid glycosides and simple polyphenols. It was speculated that the free-radical-scavenging activity increases along with the increase in polyphenols during the growth of mulberry leaves.
Based on the absorbance values of the various extract solutions, they were reacted with the Folin-Ciocalteu reagent and compared with the standard solutions of gallic acid. The total phenolic content of batch A was 57.10 mg/g, while that of batches B, C, and D was 35.69, 38.05, and 52.19 mg/g, respectively. Data obtained from the total phenolic assay supported the key role of phenolic compounds in the free radical scavenging of DPPH. As expected, the amount of total phenolics was highest in batch A, harvested in December. The results were consistent with those reported in the previous literature [24]. In the study of [24], the phenolic content in mulberry leaves was high from late May to early July. From late September, the phenolic content increased with time and reached its highest level on October 16 (the last day of the experimental period).

Preparation of Different Batches of Samples
Four batches of dried mulberry leaves harvested in different months were frozen overnight in an ultra-low-temperature refrigerator and freeze-dried to achieve a constant weight. They were fully ground and passed through an 80-mesh sieve. The dried powder (1.0 g) was extracted with a 75% ethanol solution (1:30, w/v) for half an hour in an ultrasonic bath at 35 • C. Next, the supernatant filtered from the total extract was concentrated by decompression and evaporation. The residue obtained was then dissolved in a 10 mL 80% methanol solution and filtered through a 0.22 µm PTFE membrane before the UPLC-Q-TOF-MS analysis.

Preparation of QC Samples
QC samples were prepared by mixing aliquots of four batches of samples to form a pooled sample, and they were then analyzed in the same way as the analytic samples [48].

UPLC-Q-TOF-MS Conditions
The chromatographic separation experiment was carried out using an Ultimate TM 3000 UPLC system (Thermo Scientific, DIONEX, Sunnyvale, CA, USA) equipped with an RS pump, an RS autosampler, an RS column compartment, an RS variable wavelength detector, and a compact mass spectrometer (Bruker Daltonics, Bremen, Germany) using composite ESI in the negative ion mode. The separation was operated on a Waters Acquity The optimized MS conditions were as follows: capillary voltage was 3800 V in the negative mode; dry gas (N 2 ) flow rate was 6.0 L/min; nebulizer gas (N 2 ) pressure was 0.8 bar; and dry gas temperature was set at 200 • C. The scan range was 50 to 800 Da. Sodium formate solution with a concentration of 10 mM was used as an internal calibration solution. Auto MS/MS mode was selected to collect the secondary MS data by applying different CEs with a collision gas (high-purity argon) after choosing the precursor ions. The Collision Cell RF voltage was set at 150.0 Vpp.

Data Processing
The Q-TOF-MS raw data files were first converted into the analysis base file (ABF) format by Abf Converter (version 4.0.0) and then further processed by MS-DIAL (version 4.20) [49]. Data processing included peak extraction time (0.25-45.0 min), data collection (mass scan range of 50-800 Da), MS1 mass tolerance (0.01 Da), MS2 mass tolerance (0.025 Da), peak detection, deconvolution, filtering (the peak count filter was set at 14.3%), peak alignment, and integration. A set of three-dimensional data matrices composed of RT, the mass-to-charge ratio (m/z), and peak intensity were generated.

Assay of DPPH Free Radical Scavenging Activity
DPPH radical scavenging activity was evaluated according to the method described by Sarikurkcu with slight modifications [50]. Briefly, 100 µL of the sample solution with different concentrations was mixed with 100 µL of DPPH solution (0.2 mM). The absorbance of the mixture was measured at 517 nm after 30 min of incubation in the dark at room temperature. L-ascorbic acid was used as the reference compound. The test was repeated three times, and the average value was calculated. The scavenging rate (%) of DPPH free radicals was calculated according to a formula: where Asample was the absorbance of 100 µL of sample solution and 100 µL of DPPH solution; Ablank was the absorbance of 100 µL of sample solution and 100 µL of ethanol; and Acontrol was the absorbance of 100 µL of ethanol and 100 µL of DPPH solution.

Determination of Total Phenolic Content
The total phenolic content of the extracts was analyzed using the Folin-Ciocalteu method described by Sarikurkcu with slight modifications [50]. Briefly, 5 mL of the sample solution (1.0 mg/mL of the extract) was mixed with 60 mL of ultrapure water, and then 1 mL of Folin-Ciocalteu reagent was added. The mixture was incubated for 3 min, and then 5 mL of a 10% (w/v) sodium carbonate (Na 2 CO 3 ) solution was added. The volume was set with ultrapure water at 100 mL. The final mixture was incubated for 120 min at 25 • C, and absorbance was measured at 765 nm using a spectrophotometer (UV-2550 UV-VIS, Shimadzu, Japan). The results of total phenolic content were expressed using a standard curve of gallic acid.

Statistical Analysis
The processed data were introduced into SIMCA 14.1 (Umetrics, Umeå, Sweden) for multivariate statistical analyses. All variables were pareto-scaled prior to chemometric analysis. PCA was utilized to analyze the degree of correlation of the data according to the aggregation of each batch of mulberry leaves in the group and the dispersion of different batches of mulberry leaves outside the group. Meanwhile, OPLS-DA was utilized to study the differences between the different samples, mainly for pairwise comparison. The differential components were selected according to the variable importance in projection (VIP > 5.0) obtained from the OPLS-DA model and p-values (p < 0.05) calculated by the Mann-Whitney U test. The processed data were subjected to log2 transformation. The volcano plot was obtained by using the R-4.0 language.

Conclusions
The chemical profiling of mulberry leaves harvested in different months was systematically investigated by UPLC-Q-TOF-MS. A total of 77 compounds were detected, of which 65 were tentatively identified. Flavonoid glycosides and phenolic compounds were found to be the main compounds in mulberry leaf extracts. Moreover, batch A, harvested in December, exhibited the strongest radical-scavenging activity. In contrast, batch B, harvested in March, exhibited the weakest radical-scavenging activity. It was speculated that the radical-scavenging activity was related to the polyphenols. The results obtained in this study might contribute to further investigation of mulberry leaves in terms of their potential application as food.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/molecules28041947/s1, Figure S1: Typical UPLC chromatogram detected at 254 nm, and the main compounds 1 to 7 in the figure are chlorogenic acid, rutin, isoquercitrin, quercetin-O-acetyl glucoside, astragalin, kaempferol acetyl glucoside, and linolenic acid, respectively; Figure S2: The UPLC results of the extracts of mulberry leaves with different solvents; Figure S3: The effect of ethanol concentration on the extraction (peak area) of different compounds; Figure S4: The effect of extraction method on the extraction (peak area) of different compounds; Figure S5: The effect of liquid-solid ratio on the extraction (peak area) of different compounds; Figure S6: The effect of extraction time on the extraction (peak area) of different compounds; Figure S7: The effect of extraction temperature on the extraction (peak area) of different compounds.

Data Availability Statement:
The data that support the findings of this study are available from the corresponding author upon reasonable request.